{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 基于SSRNetV2的杂草识别\n",
    "\n",
    "本项目使用自组网络SSRNetV2在杂草数据集进行训练和预测，精度为97.55%，速度为9ms，参数量为1.12M，优于MobileNetV3。\n",
    "\n",
    "# 一、项目背景\n",
    "\n",
    "为了解决农田无人自动除草，需要把杂草识别算法部署到嵌入式设备上。杂草识别任务对精度、速度和参数量有较高的要求。\n",
    "\n",
    "# 二、数据简介\n",
    "\n",
    "本项目使用的[杂草识别数据集](https://aistudio.baidu.com/aistudio/datasetdetail/96823)来自公开杂草数据，包含9个类别，8个杂草类别和1个负类。总共17509张图片，14036张训练图片和3473张验证图片。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1. 解压图片\n",
    "\n",
    "解压图片到data目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!unzip -oq -d data/ data/data96823/weeds.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2. 查看图片\n",
    "\n",
    "查看训练列表中的前三张图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图像路径: ./data/trainImageSet/20170811-110835-1.jpg 图像形状: (256, 256, 3) 标签编号： 0\n",
      "图像路径: ./data/trainImageSet/20161207-144902-0.jpg 图像形状: (256, 256, 3) 标签编号： 0\n",
      "图像路径: ./data/trainImageSet/20170704-153719-0.jpg 图像形状: (256, 256, 3) 标签编号： 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os, cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建画布\n",
    "plt.figure(figsize=(9, 3))\n",
    "\n",
    "# 绘制图像\n",
    "train_path = './data/train.txt' # 训练文件路径\n",
    "with open(train_path, 'r') as f: # 打开训练文件\n",
    "    for i, line in enumerate(f.readlines()): # 遍历每行记录\n",
    "        # 读取数据\n",
    "        if i > 2: # 读取三条数据\n",
    "            break\n",
    "        image_path, label_id = line.strip().split()                         # 读取一行记录\n",
    "        image_path = os.path.join(os.path.split(train_path)[0], image_path) # 获取图像路径\n",
    "        \n",
    "        # 读取图像\n",
    "        with open(image_path, 'rb') as f: # 打开图像文件\n",
    "            image = f.read() # 读取图像数据\n",
    "        image = np.frombuffer(image, dtype='uint8') # 读到ndarray缓存\n",
    "        image = cv2.imdecode(image, 1) # 解码为3通道图像\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 转换为RGB图像\n",
    "        \n",
    "        # 打印信息\n",
    "        print('图像路径:', image_path, '图像形状:', image.shape, '标签编号：', label_id)\n",
    "        \n",
    "        # 绘制图像\n",
    "        plt.subplot(1, 3, i + 1)\n",
    "        plt.axis('off')\n",
    "        plt.imshow(image)\n",
    "        \n",
    "# 显示图像\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3. 读取图片\n",
    "\n",
    "使用paddle.io.Dataset()接口对训练数据和验证数据进行封装，便于对数据集进行训练。训练数据首先缩放到224×224，然后进行随机裁剪、随机翻转、图片格式由HWC变换到CHW和减均值操作。验证数据首先缩放到224×224，然后进行变换图片格式HWC到CHW和减均值操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, cv2, paddle\n",
    "import numpy as np\n",
    "\n",
    "class WeedDataset(paddle.io.Dataset):\n",
    "    def __init__(self, lists_path, mode='train'):\n",
    "        \"\"\"\n",
    "        初始化数据集\n",
    "        - lists_path: 列表文件路径\n",
    "        - mode      : 数据读取模式\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        # 设置参数\n",
    "        assert os.path.exists(lists_path), f\"错误：{lists_path}不存在！\"             # 检测文件是否存在\n",
    "        assert mode in ['train', 'valid'], \"错误：数据读取模式必须为'train'或'valid'!\" # 检测数据读取模式\n",
    "\n",
    "        self.lists_path = lists_path                   # 列表文件路径\n",
    "        self.lists_dire = os.path.split(lists_path)[0] # 列表文件目录\n",
    "\n",
    "        # 读取列表\n",
    "        self.image_list = [] # 图像路径列表\n",
    "        self.label_list = [] # 标签编号列表\n",
    "\n",
    "        with open(self.lists_path, 'r') as f: # 打开列表文件\n",
    "            for line in f.readlines(): # 遍历每行记录\n",
    "                image_path, label_id = line.strip().split() # 读取一行记录\n",
    "                self.image_list.append(os.path.join(self.lists_dire, image_path)) # 添加图像路径\n",
    "                self.label_list.append(label_id)                                  # 添加标签编号\n",
    "\n",
    "        # 数据增强\n",
    "        if mode == 'train':\n",
    "            self.transforms = paddle.vision.transforms.Compose([\n",
    "                paddle.vision.transforms.Resize(size=(224, 224)),                  # 变换图像大小\n",
    "                paddle.vision.transforms.RandomCrop(size=224, padding=24),         # 随机填充裁剪\n",
    "                paddle.vision.transforms.RandomHorizontalFlip(prob=0.5),           # 随机水平翻转\n",
    "                paddle.vision.transforms.Transpose(order=(2, 0, 1)),               # 变换图像通道\n",
    "                paddle.vision.transforms.Normalize(mean=[123.675, 116.28, 103.53],\n",
    "                                                   std =[58.395 , 57.12 , 57.375]) # 图像数据归一\n",
    "            ])\n",
    "        else:\n",
    "            self.transforms = paddle.vision.transforms.Compose([\n",
    "                paddle.vision.transforms.Resize(size=(224, 224)),                  # 变换图像大小\n",
    "                paddle.vision.transforms.Transpose(order=(2, 0, 1)),               # 变换图像通道\n",
    "                paddle.vision.transforms.Normalize(mean=[123.675, 116.28, 103.53],\n",
    "                                                   std =[58.395 , 57.12 , 57.375]) # 图像数据归一\n",
    "            ])\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        获取一项数据\n",
    "        params:\n",
    "        - index: 数据索引\n",
    "        return:\n",
    "        - image: 图像数据\n",
    "        - label: 标签编号\n",
    "        \"\"\"\n",
    "        # 读取图像\n",
    "        with open(self.image_list[index], 'rb') as f: # 打开图像文件\n",
    "            image = f.read() # 读取图像数据\n",
    "        image = np.frombuffer(image, dtype='uint8') # 读到ndarray缓存\n",
    "        image = cv2.imdecode(image, 1) # 解码为3通道图片\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 转换为RGB图片\n",
    "        \n",
    "        # 读取标签\n",
    "        label = np.array(self.label_list[index], dtype='int64')\n",
    "        \n",
    "        # 增强数据\n",
    "        image = self.transforms(image)\n",
    "\n",
    "        return image, label\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回数据总数\n",
    "        \"\"\"\n",
    "        return len(self.image_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、模型组网\n",
    "\n",
    "本项目使用[SSRNetV2](http://www.ecice06.com/CN/10.19678/j.issn.1000-3428.0061329)进行组网，该网络模型使用了浅层网络和多尺度分割技术，在保持精度的条件下，极大的提升了速度，减小了模型参数量，非常适合在嵌入式设备上进行部署。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 1. 网络实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\n",
    "\n",
    "class ConvUnit(paddle.nn.Sequential):\n",
    "    def __init__(self, in_dim, ou_dim, kernel_size=1, stride=1):\n",
    "        \"\"\"\n",
    "        初始化卷积单元，带有激活函数\n",
    "        params:\n",
    "        - in_dim     (int): 输入维度\n",
    "        - ou_dim     (int): 输出维度\n",
    "        - kernel_size(int): 卷积大小\n",
    "        - stride     (int): 滑动步长\n",
    "        \"\"\"\n",
    "        super().__init__(\n",
    "            paddle.nn.Conv2D(in_dim, ou_dim, kernel_size, stride, padding='same', bias_attr=False),\n",
    "            paddle.nn.BatchNorm2D(ou_dim),\n",
    "            paddle.nn.LeakyReLU()\n",
    "        )\n",
    "        \n",
    "class ConvNorm(paddle.nn.Sequential):\n",
    "    def __init__(self, in_dim, ou_dim, kernel_size=1, stride=1):\n",
    "        \"\"\"\n",
    "        初始化卷积单元，不带激活函数\n",
    "        params:\n",
    "        - in_dim     (int): 输入维度\n",
    "        - ou_dim     (int): 输出维度\n",
    "        - kernel_size(int): 卷积大小\n",
    "        - stride     (int): 滑动步长\n",
    "        \"\"\"\n",
    "        super().__init__(\n",
    "            paddle.nn.Conv2D(in_dim, ou_dim, kernel_size, stride, padding='same', bias_attr=False),\n",
    "            paddle.nn.BatchNorm2D(ou_dim)\n",
    "        )\n",
    "\n",
    "class ProjUint(paddle.nn.Sequential):\n",
    "    def __init__(self, in_dim, ou_dim, stride=1):\n",
    "        \"\"\"\n",
    "        初始化投影单元，改变特征大小和维度\n",
    "        params:\n",
    "        - in_dim(int): 输入维度\n",
    "        - ou_dim(int): 输出维度\n",
    "        - stride(int): 滑动步长\n",
    "        \"\"\"\n",
    "        super().__init__(\n",
    "            paddle.nn.AvgPool2D(kernel_size=stride, stride=stride, padding=0),\n",
    "            paddle.nn.Conv2D(in_dim, ou_dim, kernel_size=1, stride=1, padding=0, bias_attr=False),\n",
    "            paddle.nn.BatchNorm2D(ou_dim)\n",
    "        )\n",
    "\n",
    "class SSRSplit(paddle.nn.Layer):\n",
    "    def __init__(self, in_dim, ou_dim, stride=1, splits=1):\n",
    "        \"\"\"\n",
    "        初始化分割单元\n",
    "        params:\n",
    "        - in_dim (int): 输入维度\n",
    "        - ou_dim (int): 输出维度\n",
    "        - stride (int): 滑动步长\n",
    "        - splits (int): 分割次数，分割尺度为2^n\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 输入参数检查\n",
    "        assert stride in [1, 2], '错误：滑动步长必须为1或2！'\n",
    "        assert splits >= 0     , '错误：分割次数必须大于等于0！'\n",
    "        \n",
    "        # 设置分割变量\n",
    "        self.splits = splits # 分割次数\n",
    "        self.dimensions = [] # 维度列表\n",
    "        self.split_list = [] # 分割列表\n",
    "        \n",
    "        # 添加分割列表\n",
    "        split_item = self.add_sublayer( # 添加第一个分割项目\n",
    "            'split_' + str(0),\n",
    "            ConvUnit(in_dim, ou_dim, kernel_size=3, stride=stride)\n",
    "        )\n",
    "        self.split_list.append(split_item)\n",
    "        \n",
    "        for i in range(self.splits):    # 添加剩余分的割项目\n",
    "            # 添加维度列表\n",
    "            if i < self.splits:\n",
    "                ou_dim //= 2\n",
    "                self.dimensions.append(ou_dim)\n",
    "            \n",
    "            # 添加分割列表\n",
    "            split_item = self.add_sublayer(\n",
    "                'split_' + str(i+1),\n",
    "                ConvUnit(ou_dim, ou_dim, kernel_size=3, stride=1)\n",
    "            )\n",
    "            self.split_list.append(split_item)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 提取特征\n",
    "        x_list = [] # 特征列表\n",
    "        for i, split_item in enumerate(self.split_list):\n",
    "            if i < self.splits:\n",
    "                x = split_item(x)\n",
    "                # x_item, x = paddle.split(x, num_or_sections=[-1, self.dimensions[i]], axis=1)\n",
    "                x_item, x = paddle.split(x, num_or_sections=2, axis=1) # 当使用paddelite不支持上面分割时，通道数需要设为2的次幂\n",
    "                x_list.append(x_item)\n",
    "            else:\n",
    "                x = split_item(x)\n",
    "                x_list.append(x)\n",
    "\n",
    "        # 合并特征\n",
    "        x = paddle.concat(x_list, axis=1)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "class SSRBasic(paddle.nn.Layer):\n",
    "    def __init__(self, in_dim, ch_dim, ou_dim, stride=1, splits=0, direct=True):\n",
    "        \"\"\"\n",
    "        初始化基础模块\n",
    "        params:\n",
    "        - in_dim (int): 输入维度\n",
    "        - ch_dim (int): 通道维度\n",
    "        - ou_dim (int): 输出维度\n",
    "        - stride (int): 滑动步长\n",
    "        - splits (int): 分割次数\n",
    "        - direct(bool): 直连标识\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 设置直连标识\n",
    "        self.is_pass = direct\n",
    "        \n",
    "        # 添加投影单元\n",
    "        self.project = ProjUint(in_dim, ou_dim, stride)\n",
    "        \n",
    "        # 添加卷积单元\n",
    "        self.convbn0 = ConvUnit(in_dim, ch_dim, kernel_size=1, stride=1)\n",
    "        self.convbn1 = SSRSplit(ch_dim, ch_dim, stride, splits)\n",
    "        self.convbn2 = ConvNorm(ch_dim, ou_dim, kernel_size=1, stride=1)\n",
    "        \n",
    "        # 添加激活函数\n",
    "        self.ly_relu = paddle.nn.LeakyReLU()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 直连路径\n",
    "        if self.is_pass:\n",
    "            y = x\n",
    "        else:\n",
    "            y = self.project(x)\n",
    "        \n",
    "        # 卷积路径\n",
    "        x = self.convbn0(x)\n",
    "        x = self.convbn1(x)\n",
    "        x = self.convbn2(x)\n",
    "        x = x + y\n",
    "        x = self.ly_relu(x)\n",
    "        \n",
    "        return x\n",
    "        \n",
    "class SSRBlock(paddle.nn.Layer):\n",
    "    def __init__(self, in_dim, ch_dim, ou_dim, reduce=0, repeat=0, splits=0):\n",
    "        \"\"\"\n",
    "        初始化模块结构\n",
    "        params:\n",
    "        - in_dim(int): 输入维度\n",
    "        - ch_dim(int): 通道维度\n",
    "        - ou_dim(int): 输出维度\n",
    "        - reduce(int): 缩小次数，缩小倍数为2^n\n",
    "        - repeat(int): 重复次数，必须大于等于0\n",
    "        - splits(int): 分割次数，分割尺度为2^n\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 设置输入参数\n",
    "        assert reduce >= 0, '错误：缩小次数必须大于等于0！'\n",
    "        assert repeat >= 0, '错误：重复次数必须大于等于0！'\n",
    "        \n",
    "        self.reduce = reduce # 缩小次数\n",
    "        self.repeat = repeat # 重复次数\n",
    "        \n",
    "        # 添加缩放项目\n",
    "        self.block_item0 = SSRBasic(\n",
    "            in_dim, ch_dim, ou_dim, stride=(2 if self.reduce > 0 else 1), splits=splits, direct=False\n",
    "        )\n",
    "        \n",
    "        if self.reduce > 1: # 当缩小次数大于等于2时\n",
    "            self.block_item1 = SSRBasic(ou_dim, ch_dim, ou_dim, stride=2, splits=splits, direct=False)\n",
    "        \n",
    "        # 添加重复项目\n",
    "        if self.repeat > 0: # 当重复次数大于等于1时\n",
    "            self.block_item2 = SSRBasic(ou_dim, ch_dim, ou_dim, stride=1, splits=splits, direct=True)\n",
    "            \n",
    "    def forward(self, x):\n",
    "        # 缩放特征\n",
    "        x = self.block_item0(x)\n",
    "\n",
    "        for i in range(1, self.reduce):\n",
    "            x = self.block_item1(x) # 当缩小次数大于等于2时\n",
    "            \n",
    "        # 重复特征\n",
    "        for i in range(self.repeat):\n",
    "            x = self.block_item2(x) # 当重复次数大于等于1时\n",
    "            \n",
    "        return x\n",
    "\n",
    "class SSRGroup(paddle.nn.Layer):\n",
    "    def __init__(self, group_arch):\n",
    "        \"\"\"\n",
    "        初始化模组结构\n",
    "        params:\n",
    "        - group_arch(list): 特征模组结构\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 添加模组列表\n",
    "        self.group_list = [] # 模组列表\n",
    "        for i, block_arch in enumerate(group_arch):\n",
    "            group_item = self.add_sublayer(\n",
    "                'group_' + str(i),\n",
    "                SSRBlock(\n",
    "                    in_dim=block_arch[0],\n",
    "                    ch_dim=block_arch[1],\n",
    "                    ou_dim=block_arch[2],\n",
    "                    reduce=block_arch[3],\n",
    "                    repeat=block_arch[4],\n",
    "                    splits=block_arch[5]\n",
    "                )\n",
    "            )\n",
    "            self.group_list.append(group_item)\n",
    "            \n",
    "    def forward(self, x):\n",
    "        # 提取特征\n",
    "        for group_item in self.group_list:\n",
    "            x = group_item(x)\n",
    "            \n",
    "        return x\n",
    "        \n",
    "class SSRNetV2(paddle.nn.Layer):\n",
    "    def __init__(self, num_classes=1000):\n",
    "        \"\"\"\n",
    "        初始化网络模型\n",
    "        params:\n",
    "        - num_classes(int): 分类类别数量. 如果num_classes<=0, 分类头部不会被定义. 默认值为: 1000.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 定义模型结构\n",
    "        group_arch = [ # 输入维度, 通道维度, 输出维度, 缩小次数, 重复次数, 分割次数\n",
    "            [3, 32, 128, 2, 1, 2], [128, 64, 256, 2, 1, 2], [256, 128, 512, 1, 1, 2]\n",
    "        ]                              # 特征块组结构\n",
    "        self.num_feature = 512         # 特征输出维度\n",
    "        self.num_classes = num_classes # 分类类别数量\n",
    "        \n",
    "        # 添加骨干网络\n",
    "        self.backbone = SSRGroup(group_arch)\n",
    "        \n",
    "        # 添加分类头部\n",
    "        if self.num_classes > 0:\n",
    "            self.head = paddle.nn.Sequential(\n",
    "                paddle.nn.AdaptiveAvgPool2D(output_size=1),\n",
    "                paddle.nn.Flatten(start_axis=1),\n",
    "                paddle.nn.Linear(self.num_feature, self.num_classes)\n",
    "            )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        # 提取特征\n",
    "        x = self.backbone(x)\n",
    "        \n",
    "        # 进行分类\n",
    "        if self.num_classes > 0:\n",
    "            x = self.head(x)\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2. 网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "   Layer (type)         Input Shape          Output Shape         Param #    \n",
      "===============================================================================\n",
      "    AvgPool2D-9      [[1, 3, 224, 224]]    [1, 3, 112, 112]          0       \n",
      "     Conv2D-49       [[1, 3, 112, 112]]   [1, 128, 112, 112]        384      \n",
      "  BatchNorm2D-49    [[1, 128, 112, 112]]  [1, 128, 112, 112]        512      \n",
      "     Conv2D-50       [[1, 3, 224, 224]]   [1, 32, 224, 224]         96       \n",
      "  BatchNorm2D-50    [[1, 32, 224, 224]]   [1, 32, 224, 224]         128      \n",
      "   LeakyReLU-41     [[1, 32, 224, 224]]   [1, 32, 224, 224]          0       \n",
      "     Conv2D-51      [[1, 32, 224, 224]]   [1, 32, 112, 112]        9,216     \n",
      "  BatchNorm2D-51    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128      \n",
      "   LeakyReLU-42     [[1, 32, 112, 112]]   [1, 32, 112, 112]          0       \n",
      "     Conv2D-52      [[1, 16, 112, 112]]   [1, 16, 112, 112]        2,304     \n",
      "  BatchNorm2D-52    [[1, 16, 112, 112]]   [1, 16, 112, 112]         64       \n",
      "   LeakyReLU-43     [[1, 16, 112, 112]]   [1, 16, 112, 112]          0       \n",
      "     Conv2D-53       [[1, 8, 112, 112]]    [1, 8, 112, 112]         576      \n",
      "  BatchNorm2D-53     [[1, 8, 112, 112]]    [1, 8, 112, 112]         32       \n",
      "   LeakyReLU-44      [[1, 8, 112, 112]]    [1, 8, 112, 112]          0       \n",
      "    SSRSplit-9      [[1, 32, 224, 224]]   [1, 32, 112, 112]          0       \n",
      "     Conv2D-54      [[1, 32, 112, 112]]   [1, 128, 112, 112]       4,096     \n",
      "  BatchNorm2D-54    [[1, 128, 112, 112]]  [1, 128, 112, 112]        512      \n",
      "   LeakyReLU-45     [[1, 128, 112, 112]]  [1, 128, 112, 112]         0       \n",
      "    SSRBasic-9       [[1, 3, 224, 224]]   [1, 128, 112, 112]         0       \n",
      "   AvgPool2D-10     [[1, 128, 112, 112]]   [1, 128, 56, 56]          0       \n",
      "     Conv2D-55       [[1, 128, 56, 56]]    [1, 128, 56, 56]       16,384     \n",
      "  BatchNorm2D-55     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      \n",
      "     Conv2D-56      [[1, 128, 112, 112]]  [1, 32, 112, 112]        4,096     \n",
      "  BatchNorm2D-56    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128      \n",
      "   LeakyReLU-46     [[1, 32, 112, 112]]   [1, 32, 112, 112]          0       \n",
      "     Conv2D-57      [[1, 32, 112, 112]]    [1, 32, 56, 56]         9,216     \n",
      "  BatchNorm2D-57     [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      \n",
      "   LeakyReLU-47      [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       \n",
      "     Conv2D-58       [[1, 16, 56, 56]]     [1, 16, 56, 56]         2,304     \n",
      "  BatchNorm2D-58     [[1, 16, 56, 56]]     [1, 16, 56, 56]          64       \n",
      "   LeakyReLU-48      [[1, 16, 56, 56]]     [1, 16, 56, 56]           0       \n",
      "     Conv2D-59        [[1, 8, 56, 56]]      [1, 8, 56, 56]          576      \n",
      "  BatchNorm2D-59      [[1, 8, 56, 56]]      [1, 8, 56, 56]          32       \n",
      "   LeakyReLU-49       [[1, 8, 56, 56]]      [1, 8, 56, 56]           0       \n",
      "    SSRSplit-10     [[1, 32, 112, 112]]    [1, 32, 56, 56]           0       \n",
      "     Conv2D-60       [[1, 32, 56, 56]]     [1, 128, 56, 56]        4,096     \n",
      "  BatchNorm2D-60     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      \n",
      "   LeakyReLU-50      [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       \n",
      "    SSRBasic-10     [[1, 128, 112, 112]]   [1, 128, 56, 56]          0       \n",
      "     Conv2D-62       [[1, 128, 56, 56]]    [1, 32, 56, 56]         4,096     \n",
      "  BatchNorm2D-62     [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      \n",
      "   LeakyReLU-51      [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       \n",
      "     Conv2D-63       [[1, 32, 56, 56]]     [1, 32, 56, 56]         9,216     \n",
      "  BatchNorm2D-63     [[1, 32, 56, 56]]     [1, 32, 56, 56]          128      \n",
      "   LeakyReLU-52      [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       \n",
      "     Conv2D-64       [[1, 16, 56, 56]]     [1, 16, 56, 56]         2,304     \n",
      "  BatchNorm2D-64     [[1, 16, 56, 56]]     [1, 16, 56, 56]          64       \n",
      "   LeakyReLU-53      [[1, 16, 56, 56]]     [1, 16, 56, 56]           0       \n",
      "     Conv2D-65        [[1, 8, 56, 56]]      [1, 8, 56, 56]          576      \n",
      "  BatchNorm2D-65      [[1, 8, 56, 56]]      [1, 8, 56, 56]          32       \n",
      "   LeakyReLU-54       [[1, 8, 56, 56]]      [1, 8, 56, 56]           0       \n",
      "    SSRSplit-11      [[1, 32, 56, 56]]     [1, 32, 56, 56]           0       \n",
      "     Conv2D-66       [[1, 32, 56, 56]]     [1, 128, 56, 56]        4,096     \n",
      "  BatchNorm2D-66     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      \n",
      "   LeakyReLU-55      [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       \n",
      "    SSRBasic-11      [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       \n",
      "    SSRBlock-4       [[1, 3, 224, 224]]    [1, 128, 56, 56]          0       \n",
      "   AvgPool2D-12      [[1, 128, 56, 56]]    [1, 128, 28, 28]          0       \n",
      "     Conv2D-67       [[1, 128, 28, 28]]    [1, 256, 28, 28]       32,768     \n",
      "  BatchNorm2D-67     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     \n",
      "     Conv2D-68       [[1, 128, 56, 56]]    [1, 64, 56, 56]         8,192     \n",
      "  BatchNorm2D-68     [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      \n",
      "   LeakyReLU-56      [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       \n",
      "     Conv2D-69       [[1, 64, 56, 56]]     [1, 64, 28, 28]        36,864     \n",
      "  BatchNorm2D-69     [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      \n",
      "   LeakyReLU-57      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       \n",
      "     Conv2D-70       [[1, 32, 28, 28]]     [1, 32, 28, 28]         9,216     \n",
      "  BatchNorm2D-70     [[1, 32, 28, 28]]     [1, 32, 28, 28]          128      \n",
      "   LeakyReLU-58      [[1, 32, 28, 28]]     [1, 32, 28, 28]           0       \n",
      "     Conv2D-71       [[1, 16, 28, 28]]     [1, 16, 28, 28]         2,304     \n",
      "  BatchNorm2D-71     [[1, 16, 28, 28]]     [1, 16, 28, 28]          64       \n",
      "   LeakyReLU-59      [[1, 16, 28, 28]]     [1, 16, 28, 28]           0       \n",
      "    SSRSplit-12      [[1, 64, 56, 56]]     [1, 64, 28, 28]           0       \n",
      "     Conv2D-72       [[1, 64, 28, 28]]     [1, 256, 28, 28]       16,384     \n",
      "  BatchNorm2D-72     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     \n",
      "   LeakyReLU-60      [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       \n",
      "    SSRBasic-12      [[1, 128, 56, 56]]    [1, 256, 28, 28]          0       \n",
      "   AvgPool2D-13      [[1, 256, 28, 28]]    [1, 256, 14, 14]          0       \n",
      "     Conv2D-73       [[1, 256, 14, 14]]    [1, 256, 14, 14]       65,536     \n",
      "  BatchNorm2D-73     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "     Conv2D-74       [[1, 256, 28, 28]]    [1, 64, 28, 28]        16,384     \n",
      "  BatchNorm2D-74     [[1, 64, 28, 28]]     [1, 64, 28, 28]          256      \n",
      "   LeakyReLU-61      [[1, 64, 28, 28]]     [1, 64, 28, 28]           0       \n",
      "     Conv2D-75       [[1, 64, 28, 28]]     [1, 64, 14, 14]        36,864     \n",
      "  BatchNorm2D-75     [[1, 64, 14, 14]]     [1, 64, 14, 14]          256      \n",
      "   LeakyReLU-62      [[1, 64, 14, 14]]     [1, 64, 14, 14]           0       \n",
      "     Conv2D-76       [[1, 32, 14, 14]]     [1, 32, 14, 14]         9,216     \n",
      "  BatchNorm2D-76     [[1, 32, 14, 14]]     [1, 32, 14, 14]          128      \n",
      "   LeakyReLU-63      [[1, 32, 14, 14]]     [1, 32, 14, 14]           0       \n",
      "     Conv2D-77       [[1, 16, 14, 14]]     [1, 16, 14, 14]         2,304     \n",
      "  BatchNorm2D-77     [[1, 16, 14, 14]]     [1, 16, 14, 14]          64       \n",
      "   LeakyReLU-64      [[1, 16, 14, 14]]     [1, 16, 14, 14]           0       \n",
      "    SSRSplit-13      [[1, 64, 28, 28]]     [1, 64, 14, 14]           0       \n",
      "     Conv2D-78       [[1, 64, 14, 14]]     [1, 256, 14, 14]       16,384     \n",
      "  BatchNorm2D-78     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "   LeakyReLU-65      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    SSRBasic-13      [[1, 256, 28, 28]]    [1, 256, 14, 14]          0       \n",
      "     Conv2D-80       [[1, 256, 14, 14]]    [1, 64, 14, 14]        16,384     \n",
      "  BatchNorm2D-80     [[1, 64, 14, 14]]     [1, 64, 14, 14]          256      \n",
      "   LeakyReLU-66      [[1, 64, 14, 14]]     [1, 64, 14, 14]           0       \n",
      "     Conv2D-81       [[1, 64, 14, 14]]     [1, 64, 14, 14]        36,864     \n",
      "  BatchNorm2D-81     [[1, 64, 14, 14]]     [1, 64, 14, 14]          256      \n",
      "   LeakyReLU-67      [[1, 64, 14, 14]]     [1, 64, 14, 14]           0       \n",
      "     Conv2D-82       [[1, 32, 14, 14]]     [1, 32, 14, 14]         9,216     \n",
      "  BatchNorm2D-82     [[1, 32, 14, 14]]     [1, 32, 14, 14]          128      \n",
      "   LeakyReLU-68      [[1, 32, 14, 14]]     [1, 32, 14, 14]           0       \n",
      "     Conv2D-83       [[1, 16, 14, 14]]     [1, 16, 14, 14]         2,304     \n",
      "  BatchNorm2D-83     [[1, 16, 14, 14]]     [1, 16, 14, 14]          64       \n",
      "   LeakyReLU-69      [[1, 16, 14, 14]]     [1, 16, 14, 14]           0       \n",
      "    SSRSplit-14      [[1, 64, 14, 14]]     [1, 64, 14, 14]           0       \n",
      "     Conv2D-84       [[1, 64, 14, 14]]     [1, 256, 14, 14]       16,384     \n",
      "  BatchNorm2D-84     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     \n",
      "   LeakyReLU-70      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    SSRBasic-14      [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       \n",
      "    SSRBlock-5       [[1, 128, 56, 56]]    [1, 256, 14, 14]          0       \n",
      "   AvgPool2D-15      [[1, 256, 14, 14]]     [1, 256, 7, 7]           0       \n",
      "     Conv2D-85        [[1, 256, 7, 7]]      [1, 512, 7, 7]        131,072    \n",
      "  BatchNorm2D-85      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "     Conv2D-86       [[1, 256, 14, 14]]    [1, 128, 14, 14]       32,768     \n",
      "  BatchNorm2D-86     [[1, 128, 14, 14]]    [1, 128, 14, 14]         512      \n",
      "   LeakyReLU-71      [[1, 128, 14, 14]]    [1, 128, 14, 14]          0       \n",
      "     Conv2D-87       [[1, 128, 14, 14]]     [1, 128, 7, 7]        147,456    \n",
      "  BatchNorm2D-87      [[1, 128, 7, 7]]      [1, 128, 7, 7]          512      \n",
      "   LeakyReLU-72       [[1, 128, 7, 7]]      [1, 128, 7, 7]           0       \n",
      "     Conv2D-88        [[1, 64, 7, 7]]       [1, 64, 7, 7]         36,864     \n",
      "  BatchNorm2D-88      [[1, 64, 7, 7]]       [1, 64, 7, 7]           256      \n",
      "   LeakyReLU-73       [[1, 64, 7, 7]]       [1, 64, 7, 7]            0       \n",
      "     Conv2D-89        [[1, 32, 7, 7]]       [1, 32, 7, 7]          9,216     \n",
      "  BatchNorm2D-89      [[1, 32, 7, 7]]       [1, 32, 7, 7]           128      \n",
      "   LeakyReLU-74       [[1, 32, 7, 7]]       [1, 32, 7, 7]            0       \n",
      "    SSRSplit-15      [[1, 128, 14, 14]]     [1, 128, 7, 7]           0       \n",
      "     Conv2D-90        [[1, 128, 7, 7]]      [1, 512, 7, 7]        65,536     \n",
      "  BatchNorm2D-90      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "   LeakyReLU-75       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    SSRBasic-15      [[1, 256, 14, 14]]     [1, 512, 7, 7]           0       \n",
      "     Conv2D-92        [[1, 512, 7, 7]]      [1, 128, 7, 7]        65,536     \n",
      "  BatchNorm2D-92      [[1, 128, 7, 7]]      [1, 128, 7, 7]          512      \n",
      "   LeakyReLU-76       [[1, 128, 7, 7]]      [1, 128, 7, 7]           0       \n",
      "     Conv2D-93        [[1, 128, 7, 7]]      [1, 128, 7, 7]        147,456    \n",
      "  BatchNorm2D-93      [[1, 128, 7, 7]]      [1, 128, 7, 7]          512      \n",
      "   LeakyReLU-77       [[1, 128, 7, 7]]      [1, 128, 7, 7]           0       \n",
      "     Conv2D-94        [[1, 64, 7, 7]]       [1, 64, 7, 7]         36,864     \n",
      "  BatchNorm2D-94      [[1, 64, 7, 7]]       [1, 64, 7, 7]           256      \n",
      "   LeakyReLU-78       [[1, 64, 7, 7]]       [1, 64, 7, 7]            0       \n",
      "     Conv2D-95        [[1, 32, 7, 7]]       [1, 32, 7, 7]          9,216     \n",
      "  BatchNorm2D-95      [[1, 32, 7, 7]]       [1, 32, 7, 7]           128      \n",
      "   LeakyReLU-79       [[1, 32, 7, 7]]       [1, 32, 7, 7]            0       \n",
      "    SSRSplit-16       [[1, 128, 7, 7]]      [1, 128, 7, 7]           0       \n",
      "     Conv2D-96        [[1, 128, 7, 7]]      [1, 512, 7, 7]        65,536     \n",
      "  BatchNorm2D-96      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     \n",
      "   LeakyReLU-80       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    SSRBasic-16       [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       \n",
      "    SSRBlock-6       [[1, 256, 14, 14]]     [1, 512, 7, 7]           0       \n",
      "    SSRGroup-2       [[1, 3, 224, 224]]     [1, 512, 7, 7]           0       \n",
      "AdaptiveAvgPool2D-2   [[1, 512, 7, 7]]      [1, 512, 1, 1]           0       \n",
      "     Flatten-4        [[1, 512, 1, 1]]         [1, 512]              0       \n",
      "     Linear-2            [[1, 512]]             [1, 9]             4,617     \n",
      "===============================================================================\n",
      "Total params: 1,179,145\n",
      "Trainable params: 1,159,337\n",
      "Non-trainable params: 19,808\n",
      "-------------------------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 215.36\n",
      "Params size (MB): 4.50\n",
      "Estimated Total Size (MB): 220.44\n",
      "-------------------------------------------------------------------------------\n",
      "\n",
      "输出形状： [1, 9]\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "\n",
    "# 定义模型\n",
    "num_classes = 9                         # 类别数量\n",
    "model = SSRNetV2(num_classes)           # 声明模型\n",
    "paddle.summary(model, (1, 3, 224, 224)) # 模型参数\n",
    "\n",
    "# 输入数据\n",
    "image = paddle.randn(shape=[1, 3, 224, 224], dtype='float32')\n",
    "\n",
    "# 处理数据\n",
    "infer = model(image)\n",
    "\n",
    "# 输出特征\n",
    "print('输出形状：', infer.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 三、网络训练\n",
    "\n",
    "本项目使用线性预热加余弦退火策略进行训练，前5轮进行线性预热，学习率由0增加到0.5，后595轮学习率由0.5降到0。总共训练600轮，每批次为128。使用Momentum优化器进行训练，并使用L2正则化，正则化系数为0.0001。\n",
    "\n",
    "## 1. 网络训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, math\n",
    "import paddle, visualdl\n",
    "\n",
    "# 设置参数\n",
    "batch_size = 128                                     # 数据批次大小\n",
    "epoch_num  = 602                                     # 训练轮次总数\n",
    "eval_freq  = 10                                      # 验证数据频率\n",
    "epoch_lin  = 5                                       # 线性预热轮数\n",
    "lrate_max  = 0.5                                     # 最大学习率值\n",
    "lrate_min  = 0.0                                     # 最小学习率值\n",
    "l2_decay   = 0.0001                                  # 权重衰减系数\n",
    "momentum   = 0.9                                     # 优化器动量值\n",
    "train_path = './data/train.txt'                      # 训练数据路径\n",
    "valid_path = './data/valid.txt'                      # 验证数据路径\n",
    "save_path  = './out/'                                # 模型保存路径\n",
    "logs_path  = './log/'                                # 日志保存路径\n",
    "train_dataset = WeedDataset(train_path, 'train')     # 训练的数据集\n",
    "valid_dataset = WeedDataset(valid_path, 'valid')     # 验证的数据集\n",
    "iters_num = math.ceil(len(train_dataset)/batch_size) # 每轮迭代次数\n",
    "cos_iters = iters_num * (epoch_num - epoch_lin)      # 余弦衰减迭数\n",
    "lin_iters = iters_num * epoch_lin                    # 线性预热迭数\n",
    "\n",
    "# 声明模型\n",
    "num_classes = 9               # 类别数量\n",
    "model = SSRNetV2(num_classes) # 声明模型\n",
    "model = paddle.Model(model)   # 封装模型\n",
    "\n",
    "# 优化算法\n",
    "cos_lrate = paddle.optimizer.lr.CosineAnnealingDecay(lrate_max, cos_iters, lrate_min)    # 余弦学习率\n",
    "scheduler = paddle.optimizer.lr.LinearWarmup(cos_lrate, lin_iters, lrate_min, lrate_max) # 线性学习率\n",
    "optimizer = paddle.optimizer.Momentum(                                                   # 优化器算法\n",
    "    learning_rate=scheduler,\n",
    "    momentum=momentum,\n",
    "    weight_decay=paddle.regularizer.L2Decay(l2_decay),\n",
    "    parameters=model.parameters()\n",
    ")\n",
    "\n",
    "#############################################################################################################\n",
    "\n",
    "# 回调日志\n",
    "class CallLogs(paddle.callbacks.Callback):\n",
    "    def __init__(self, logs_path, save_path):\n",
    "        \"\"\"\n",
    "        初始化记录器\n",
    "        params:\n",
    "        - logs_path(str): 日志路径\n",
    "        - save_path(str): 模型路径\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        # 清空日志目录\n",
    "        for root, dirs, files in os.walk(logs_path, topdown=False): # 遍历目录\n",
    "            for name in files:\n",
    "                os.remove(os.path.join(root, name))                 # 删除文件\n",
    "            for name in dirs:\n",
    "                os.rmdir(os.path.join(root, name))                  # 删除目录\n",
    "\n",
    "        # 设置日志变量\n",
    "        self.logs_path = logs_path                                  # 日志路径\n",
    "        self.save_path = save_path                                  # 模型路径\n",
    "        self.trian = visualdl.LogWriter(self.logs_path + 'train/')  # 训练日志\n",
    "        self.valid = visualdl.LogWriter(self.logs_path + 'valid/')  # 验证日志\n",
    "        self.best_epoch = 0                                         # 最好轮数\n",
    "        self.best_loss  = 1e6                                       # 最好损失\n",
    "        self.best_acc   = 0.0                                       # 最好精度\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs):\n",
    "        \"\"\"\n",
    "        轮次结束调用\n",
    "        params:\n",
    "        - epoch(int): 当前轮数\n",
    "        - logs(dict): 日志字典\n",
    "        \"\"\"\n",
    "        # 添加训练标量\n",
    "        self.epoch = epoch + 1                                                          # 设置当前轮数\n",
    "        self.trian.add_scalar(tag='Trian/Loss', step=self.epoch, value=logs['loss'][0]) # 添加训练损失\n",
    "        self.trian.add_scalar(tag='Trian/Accuracy', step=self.epoch, value=logs['acc']) # 添加训练精度\n",
    "\n",
    "    def on_eval_end(self, logs):\n",
    "        \"\"\"\n",
    "        验证结束调用\n",
    "        params:\n",
    "        - logs(dict): 日志字典\n",
    "        \"\"\"\n",
    "        # 添加验证标量\n",
    "        self.valid.add_scalar(tag='Trian/Loss', step=self.epoch, value=logs['loss'][0]) # 添加验证损失\n",
    "        self.valid.add_scalar(tag='Trian/Accuracy', step=self.epoch, value=logs['acc']) # 添加验证精度\n",
    "\n",
    "        # 保存最好参数\n",
    "        if logs['acc'] >= self.best_acc:              # 是否最好精度\n",
    "            self.best_epoch = self.epoch              # 更新最好轮数\n",
    "            self.best_loss  = logs['loss'][0]         # 更新最好损失\n",
    "            self.best_acc   = logs['acc']             # 更新最好精度\n",
    "            self.model.save(self.save_path + 'great') # 保存训练参数\n",
    "\n",
    "    def on_train_end(self, logs):\n",
    "        \"\"\"\n",
    "        训练结束调用\n",
    "        params:\n",
    "        - logs(dict): 日志字典\n",
    "        \"\"\"\n",
    "        message = f'ENDED - best epoch: {self.best_epoch}, '+\\\n",
    "                  f'best loss: {self.best_loss:.6f}, '+\\\n",
    "                  f'best accuracy: {self.best_acc:.4f}, '+\\\n",
    "                  f'logs path: {self.logs_path}'            # 保存信息\n",
    "        print(message)                                      # 打印信息\n",
    "        with open(self.logs_path + 'log.txt', 'w') as f:    # 打开文件\n",
    "            f.write(message + '\\n')                         # 写入信息\n",
    "\n",
    "call_logs = CallLogs(logs_path, save_path) # 回调日志\n",
    "\n",
    "#############################################################################################################\n",
    "\n",
    "# 配置模型\n",
    "model.prepare(\n",
    "    optimizer=optimizer,               # 优化算法\n",
    "    loss=paddle.nn.CrossEntropyLoss(), # 损失函数\n",
    "    metrics=paddle.metric.Accuracy()   # 评估方法\n",
    ")\n",
    "\n",
    "# 训练模型\n",
    "model.fit(\n",
    "    train_dataset,         # 训练数据\n",
    "    valid_dataset,         # 验证数据\n",
    "    batch_size=batch_size, # 批次大小\n",
    "    epochs    =epoch_num,  # 训练轮次\n",
    "    eval_freq =eval_freq,  # 验证频率\n",
    "    save_dir  =save_path,  # 保存路径\n",
    "    save_freq =epoch_num,  # 保存频率\n",
    "    verbose   =1,          # 打印日志\n",
    "    shuffle   =True,       # 打乱数据\n",
    "    callbacks =call_logs   # 回调日志\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 四、模型保存\n",
    "\n",
    "## 1. 网络保存\n",
    "\n",
    "把动态图训练的模型和权重保存为静态图的模型和权重，便于在嵌入式设备上进行移植。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import paddle\n",
    "\n",
    "# 设置参数\n",
    "load_path = './out/great.pdparams' # 模型权重路径\n",
    "save_path = './out/model'          # 模型保存路径\n",
    "\n",
    "# 声明模型\n",
    "num_classes = 9                              # 类别数量\n",
    "model = SSRNetV2(num_classes)                # 声明模型\n",
    "model.set_state_dict(paddle.load(load_path)) # 加载权重\n",
    "\n",
    "# 保存模型\n",
    "paddle.jit.save(\n",
    "    model,\n",
    "    save_path,\n",
    "    input_spec=[paddle.static.InputSpec([None, 3, 224, 224], 'float32', 'x')]\n",
    ")\n",
    "\n",
    "print('save path:', save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2. 网络验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eval begin...\n",
      "The loss value printed in the log is the current batch, and the metric is the average value of previous step.\n",
      "step 3473/3473 [==============================] - acc: 0.9755 - 9ms/step        \n",
      "Eval samples: 3473\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "\n",
    "# 设置参数\n",
    "valid_path = './data/valid.txt' # 验证数据路径\n",
    "model_path = './out/model'      # 模型加载路径\n",
    "\n",
    "# 读取数据\n",
    "valid_dataset = WeedDataset(valid_path, 'valid') # 验证数据\n",
    "\n",
    "# 声明模型\n",
    "model = paddle.jit.load(model_path)                                                              # 加载模型\n",
    "model = paddle.Model(model, inputs=paddle.static.InputSpec([None, 3, 224, 224], 'float32', 'x')) # 封装模型\n",
    "\n",
    "# 配置模型\n",
    "model.prepare(metrics=paddle.metric.Accuracy())\n",
    "\n",
    "# 评估模型\n",
    "result = model.evaluate(valid_dataset, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 3. 网络预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict begin...\n",
      "step 3473/3473 [==============================] - 9ms/step        \n",
      "Predict samples: 3473\n",
      "sample: 1, infer: 0\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "\n",
    "# 设置参数\n",
    "valid_path = './data/valid.txt' # 验证数据路径\n",
    "model_path = './out/model'      # 模型加载路径\n",
    "\n",
    "# 读取数据\n",
    "valid_dataset = WeedDataset(valid_path, 'valid') # 验证数据\n",
    "\n",
    "# 声明模型\n",
    "model = paddle.jit.load(model_path)                                                              # 加载模型\n",
    "model = paddle.Model(model, inputs=paddle.static.InputSpec([None, 3, 224, 224], 'float32', 'x')) # 封装模型\n",
    "\n",
    "# 配置模型\n",
    "model.prepare()\n",
    "\n",
    "# 模型预测\n",
    "result = model.predict(valid_dataset)\n",
    "\n",
    "# 显示结果\n",
    "sample = 1 # 样本编号\n",
    "print(f'sample: {sample}, infer: {np.argmax(result[0][sample - 1])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 五、项目总结\n",
    "\n",
    "本项目为杂草图像识别任务，使用SSRNetV2能取得了较好的识别精度和速度，并且模型参数量也较小，比较适合部署在嵌入式设备上。\n",
    "\n",
    "# 六、个人简介\n",
    "三峡大学 计算机与信息学院 研究生 研究方法：人工智能、嵌入式系统、计算机视觉\n",
    "\n",
    "[盛夏夜博客](https://www.cnblogs.com/d442130165/)\n",
    "\n",
    "[盛夏夜码云](https://gitee.com/shengxiaye)"
   ]
  }
 ],
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